Choose a task with a finish line
A note based on public documents, a letter to edit or a summary to review gives a tangible result. Define what makes the output usable: complete content, relevant sources, layout, file formats and human approval. A pleasant answer is not enough if the document must then be rebuilt from scratch.
Define data and responsibilities
For the French context, CNIL recommends framing generative AI use, data and risks. [1] We suggest involving the professional lead, IT team and data protection function before sensitive tests. Translating this guide does not mean identical legal requirements apply in every country.
Prepare the environment
Start with synthetic datasets and approved documents. Check accounts, roles, shared folders, data destinations and recovery from errors. Knowing an identifier should not let an employee access another team’s work. The pilot should not become an improvised experiment on the public production server.
Test the document, not just the first answer
Request a first version, edit one section, inspect a source and open the final Word or PDF. Repeat after an interruption. References should remain accessible, changes understandable and unknown fields identified. An edit must not carry the certificate of an older version.
Measure observable value
Agree beforehand on expected quality, acceptable rework, total cost and unacceptable errors. Compare the normal method with the APLOMB workflow on comparable cases. Keep failed and incomplete tasks in the evaluation instead of showing only the best examples.
Decide what comes next
Separate what works locally, what has been tested with providers and what is validated in the authority’s environment. Continuing, stopping or expanding the pilot should be documented decisions. APLOMB proposes building this scope with your teams; availability and terms remain to be confirmed together.
External references
Translations describe the same scope; they do not extend legal or technical coverage.